System and method for performance-based routing of interactions in a contact center

ABSTRACT

A system and method for performance-based routing of interactions in a contact center. A routing server receives information on an interaction to be routed, and identifies a call reason for the interaction. The identification of the call reason may be based for example, speech analytics. The routing server identifies one or more agents having experience in handling the topic. The routing server further determines a proficiency level of the identified agents in handling the topic, and selects one of the identified agents having at least a minimum level of proficiency. The routing server transmits a message for routing the interaction to the selected agent.

BACKGROUND

Businesses use contact centers that include automated systems andrepresentatives of the business to process transactions and/or servicethe needs of their customers. Contact centers may use a number ofcommunication channels to engage the customers, such as telephone,email, live web chat, and the like.

An interaction from a customer such as, for example, an inbound voicecall, is routed to a contact center target, such as, for example, acontact center agent, for processing. In simplest terms, routing is aprocess of sending the interaction to the target, such as, for example,sending an incoming telephone call or an incoming e-mail, to an agent.In practice, many steps are taken between the arrival of the interactionand the selection and use of a target. Generally speaking, not allinteractions should go to the same target; choices should be made inorder to determine the best target for each interaction. Eachchoice-point is an opportunity to make a decision based on the currentsituation—with the general goal of getting the interaction delivered tothe right target.

SUMMARY

Embodiments of the present invention are directed to a system and methodfor performance-based routing of interactions in a contact center. Aprocessor receives information on an interaction to be routed. Theprocessor identifies a topic associated with the interaction, andidentifies one or more agents having experience handling the topic. Theprocessor further determines a proficiency level of the identifiedagents in handling the topic, and selects one of the identified agentshaving at least a minimum level of proficiency in handling the topic.The processor transmits a message for routing the interaction to theselected agent.

According to one embodiment, the proficiency level of a particular agentis calculated based on past interactions by the particular agent inhandling interactions with a same topic.

According to one embodiment, the proficiency level of a particular agentis indicative of the ability of the particular agent to resolve thetopic a first time the topic is raised in an interaction. In thisregard, according to one embodiment, the processor recognizes a phraseuttered by a customer or agent during a past interaction, categorizesthe past interaction as being unresolved based on the recognized phrase,and calculates the proficiency level based on a number of pastinteractions categorized as being unresolved.

According to one embodiment, the proficiency level of a particular agentis indicative of a time it takes the agent in handling interactionsassociated with the topic.

According to one embodiment, the topic is identified as a specific agentskill, wherein identifying by the processor the one or more agentsincludes identifying by the processor one or more agents with thespecific agent skill enabled.

According to one embodiment, the selecting of one of the identifiedagents having at least a minimum level of proficiency includesidentifying, by the processor, a threshold performance value; comparing,by the processor, the proficiency level of the identified agents againstthe threshold performance value; and selecting, by the processor, theone of the identified agents based on the comparison.

According to one embodiment, the identifying of the topic includesrecognizing a phrase uttered by a customer or agent; and identifying thetopic based on the recognized phrase. The phrase may be uttered duringinteraction by the customer with an interactive media response server.

As a person of skill in the art should appreciate, embodiments of thepresent invention provide an interaction handling mechanism that allowdeducing a caller's reason for a call at a higher level of granularitythan what may be achieved via traditional mechanisms alone, and for thatcall for which the reason may be deduced at the finer level ofgranularity, route the call to an agent who has proven, based on pasthandling of similar interactions, to be able to actually handle thecurrent call than another agent who may have proven to be less qualifiedto handle the call. Embodiments of the present invention therefore allowbetter service to be provided to the caller.

These and other features, aspects and advantages of the presentinvention will be more fully understood when considered with respect tothe following detailed description, appended claims, and accompanyingdrawings. Of course, the actual scope of the invention is defined by theappended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of a system supporting a contactcenter for routing interactions based on agent proficiency according toone exemplary embodiment of the invention;

FIG. 2 is a flow diagram of a process for adding, adjusting, and/orremoving a skill or sub-skill for a particular agent according to oneembodiment of the invention;

FIG. 3 is a conceptual layout diagram of exemplary skills and sub-skillsthat may be enabled or disabled for a specific agent according to oneembodiment of the invention;

FIG. 4 is a conceptual layout diagram of a call distribution tableaccording to one embodiment of the invention;

FIG. 5 is a conceptual layout diagram of a process engaged by a speechanalytics module for assigning topics based on specific spoken phrasesdetected during a conversation between a customer and an agent accordingto one embodiment of the invention;

FIG. 6 is a flow diagram of a process for performance-based routing ofinteractions to agents according to one embodiment of the invention;

FIG. 7 is a conceptual layout diagram of exemplary proficiency levels ofagents where the proficiency is first call resolution for a particularcall topic according to one embodiment of the invention;

FIG. 8A is a block diagram of a computing device according to anembodiment of the present invention.

FIG. 8B is a block diagram of a computing device according to anembodiment of the present invention.

FIG. 8C is a block diagram of a computing device according to anembodiment of the present invention.

FIG. 8D is a block diagram of a computing device according to anembodiment of the present invention.

FIG. 8E is a block diagram of a network environment including severalcomputing devices according to an embodiment of the present invention.

DETAILED DESCRIPTION

In general terms, embodiments of the present invention are directed to asystem and method for routing interactions with specifically recognizedissues, to agents deemed to have actual proficiency in handling thoseissues. According to one embodiment, the specific issues are recognizedby deducing a caller's reason for a call at a finer level of granularitythan what is achieved via traditional mechanisms alone. The granularcall reason may be deduced, for example, by recognizing specific phrasesuttered by a caller and/or agent during a current call, or based onutterances spoken and recognized by a speech analytics system during aprior call. Other mechanisms in addition or in lieu of speech analyticsmay be invoked to deduce/infer the granular call reason. For example,information gathered from the caller interacting with a self-help systemor other communication channel(s) (e.g. web, chat, email, social mediamessage, etc.) may be used to deduce a particular call reason(s).

For those calls for which a granular call reason may be deduced, thecall is routed to an agent who has proven to have, based on pasthandling of other calls with the same call reason, actual proficiency inhandling the call reason. According to one embodiment, an agent's actualperformance or proficiency is measured in terms of one or moreperformance metrics. A particular performance metric may measure, forexample, a number (e.g. percentage) of unresolved calls, an averagehandling time, and/or customer satisfaction resulting from the agenthandling calls with a particular call topic. In this manner, availableagents whose performance metric(s) indicate that they are qualified tohandle a particular type of call are selected to handle the call insteadof other agents whose performance metric(s) may indicate that they areless qualified to handle the call.

FIG. 1 is a schematic block diagram of a system supporting a contactcenter for routing interactions based on agent proficiency according toone exemplary embodiment of the invention. The contact center may be anin-house facility to a business or corporation for serving theenterprise in performing the functions of sales and service relative tothe products and services available through the enterprise. In anotheraspect, the contact center may be a third-party service provider. Thecontact center may be deployed locally in equipment dedicated to theenterprise or third-party service provider, and/or deployed in a remotecomputing environment such as, for example, a private or public cloudenvironment with infrastructure for supporting multiple contact centersfor multiple enterprises.

According to one exemplary embodiment, the contact center includesresources (e.g. personnel, computers, and telecommunication equipment)to enable delivery of services via telephone or other communicationmechanisms. Such services may vary depending on the type of contactcenter, and may range from customer service to help desk, emergencyresponse, telemarketing, order taking, and the like.

Customers, potential customers, or other end users (collectivelyreferred to as customers) desiring to receive services from the contactcenter may initiate inbound interactions, such as, for example, calls,to the contact center, via their end user devices 10 a-10 c(collectively referenced as 10). Each of the end user devices 10 may bea communication device conventional in the art, such as, for example, atelephone, wireless phone, smart phone, personal computer, electronictablet, and/or the like. Users operating the end user devices 10 mayinitiate, manage, and respond to telephone calls, emails, chats, textmessaging, web-browsing sessions, and other multi-media transactions.

Inbound and outbound calls from and to the end users devices 10 maytraverse a telephone, cellular, and/or data communication network 14depending on the type of device that is being used. For example, thecommunications network 14 may include a private or public switchedtelephone network (PSTN), local area network (LAN), private wide areanetwork (WAN), and/or public wide area network such as, for example, theInternet. The communications network 14 may also include a wirelesscarrier network including a code division multiple access (CDMA)network, global system for mobile communications (GSM) network, and/orany 3G or 4G network conventional in the art.

According to one exemplary embodiment, the contact center includes aswitch/media gateway 12 coupled to the communications network 14 forreceiving and transmitting calls between end users and the contactcenter. The switch/media gateway 12 may include a telephony switchconfigured to function as a central switch for agent level routingwithin the center. In this regard, the switch 12 may include anautomatic call distributor, a private branch exchange (PBX), an IP-basedsoftware switch, and/or any other switch configured to receiveInternet-sourced calls and/or telephone network-sourced calls. Accordingto one exemplary embodiment of the invention, the switch is coupled to acall server 18 which may, for example, serve as an adapter or interfacebetween the switch and the remainder of the routing, monitoring, andother call-handling components of the contact center.

The contact center may also include a multimedia/social media server 24for engaging in media interactions other than voice interactions withthe end user devices 10 and/or web servers 32. The media interactionsmay be related, for example, to email, vmail (voice mail through email),chat, video, text-messaging, web, social media, screen-sharing, and thelike. The web servers 32 may include, for example, social interactionsite hosts for a variety of known social interaction sites to which anend user may subscribe, such as, for example, Facebook, Twitter, and thelike. The web servers may also provide web pages for the enterprise thatis being supported by the contact center. End users may browse the webpages and get information about the enterprise's products and services.The web pages may also provide a mechanism for contacting the contactcenter, via, for example, web chat, voice call, email, web real timecommunication (WebRTC), or the like.

According to one exemplary embodiment of the invention, the switch iscoupled to an interactive media response (IMR) server 34, which may alsobe referred to as a self-help system, virtual assistant, or the like.The IMR server 34 may be similar to an interactive voice response (IVR)server, except that the IMR server is not restricted to voice, but maycover a variety of media channels including voice. Taking voice as anexample, however, the IMR server may be configured with an IMR scriptfor querying calling customers on their needs. For example, a contactcenter for a bank may tell callers, via the IMR script, to “press 1” ifthey wish to get an account balance. If this is the case, throughcontinued interaction with the IMR, customers may complete servicewithout needing to speak with an agent. The IMR server 34 may also askan open ended question such as, for example, “How can I help you?” andthe customer may speak or otherwise enter a reason for contacting thecontact center. The customer's response may then be used by the routingserver 20 to route the call to an appropriate contact center resource.

If the call is to be routed to an agent, the call is forwarded to thecall server 18 which interacts with a routing server 20 for finding anappropriate agent for processing the call. The call server 18 may beconfigured to process PSTN calls, VoIP calls, and the like. For example,the call server 18 may include a session initiation protocol (SIP)server for processing SIP calls. According to some exemplaryembodiments, the call server 18 may, for example, extract data about thecustomer interaction such as the caller's telephone number, often knownas the automatic number identification (ANI) number, or the customer'sinternet protocol (IP) address, or email address.

In some embodiments, the routing server 20 may query a customerdatabase, which stores information about existing clients, such ascontact information, service level agreement (SLA) requirements, natureof previous customer contacts and actions taken by contact center toresolve any customer issues, and the like. The database may be managedby any database management system conventional in the art, such asOracle, IBM DB2, Microsoft SQL server, Microsoft Access, PostgreSQL,MySQL, FoxPro, and SQLite, and may be stored in a mass storage device30. The routing server 20 may query the customer information from thecustomer database via an ANI or any other information collected by theIMR 34 and forwarded to the routing server by the call server 18.

Once an appropriate agent is available to handle a call, a connection ismade between the caller and an agent device 38 a-38 c (collectivelyreferenced as 38) of the identified agent. Collected information aboutthe caller and/or the caller's historical information may also beprovided to the agent device for aiding the agent in better servicingthe call. In this regard, each agent device 38 may include a telephoneadapted for regular telephone calls, VoIP calls, and the like. The agentdevice 38 may also include a computer for communicating with one or moreservers of the contact center and performing data processing associatedwith contact center operations, and for interfacing with customers viavoice and other multimedia communication mechanisms.

The selection of an appropriate agent for routing an inbound call may bebased, for example, on a routing strategy employed by the routing server20, and further based on information about agent availability, skills,and other routing parameters provided, for example, by a statisticsserver 22. According to various embodiments of the present invention,the selection of the agent is also based on the agent's proficiencylevel in handling a specific topic identified for the call.

The contact center may also include a reporting server 28 configured togenerate reports from data aggregated by the statistics server 22. Suchreports may include near real-time reports or historical reportsconcerning the state of resources, such as, for example, average waitingtime, abandonment rate, agent occupancy, and the like. For example, thereports may organize the information based on agent skills and/or skilllevels. Call distribution reports may also be generated based oncategorization of calls into specific call topics. The call distributionreports may indicate a number or percentage of calls with a specificcall topic handled by the contact center for a particular time period.The reports may be generated automatically or in response to specificrequests from a requestor (e.g. agent/administrator, contact centerapplication, and/or the like).

According to one exemplary embodiment of the invention, the routingserver 20 is enhanced with functionality for managingback-office/offline activities that are assigned to the agents. Suchactivities may include, for example, responding to emails, responding toletters, attending training seminars, or any other activity that doesnot entail real time communication with a customer. Selection of agentsto handle these types of activities may also depend on proficiencylevels of agents in handling specific issues identified for theactivities. Once assigned to an agent, an activity may be pushed to theagent, or may appear in the agent's workbin 26 a-26 c (collectivelyreferenced as 26) as a task to be completed by the agent. The agent'sworkbin may be implemented via any data structure conventional in theart, such as, for example, a linked list, array, and/or the like. Theworkbin may be maintained, for example, in buffer memory of each agentdevice 38.

According to one exemplary embodiment of the invention, the mass storagedevice(s) 30 may store one or more databases relating to agent data(e.g. agent profiles, schedules, etc.), customer data (e.g. customerprofiles), interaction data (e.g. details of each interaction with acustomer, including reason for the interaction, disposition data, timeon hold, handling time, etc.), and the like. The one or more databasesstoring a customer's profile and interaction/case data may generally bereferred to as a customer database. According to one embodiment, some ofthe data (e.g. customer profile data) may be provided by a third partydatabase such as, for example, a third party customer relationsmanagement (CRM) database. The mass storage device may take form of ahard disk or disk array as is conventional in the art.

According to one embodiment, the system also includes an analyticsserver 25 which in turn includes a speech analytics module 40, callinference module 42, and a performance metric module 44. Each module maybe implemented via computer instructions that are stored in memory andexecuted by a processor for providing particular functionality. Forexample, the speech analytics module 40 may contain instructions foranalyzing audio of real-time or recorded calls, and storing the analysisdata in the mass storage devise 30. The analysis data may be, forexample, classification of calls into predefined categories based onrecognition of one or more phrases uttered by a customer. For example, acall may be classified as being “unresolved,” upon detecting phrasessuch as, for example, “I'll call again later,” A call may also becategorized as having a particular call topic such as, for example,“where is my stuff,” based on recognition of phrases such as, forexample, “I would like to check the status of my order.”

The call inference module 42 may contain instructions for interactingwith various contact center resources, such as, for example, the IMRserver 34, speech analytics module 40, and/or one or more databases inthe mass storage device 30, for deducing a reason for an incoming call,and storing the call reason in the customer database. The call inferencemodule 42 may also be configured to populate a call distribution tableused to collect a list of different call topics deduced from callsreceived by the contact center. The call distribution table may also bepopulated by other modules, such as, for example, the speech analyticsmodule 40.

The terms “call” and “call reason” are used herein to refer to all typesof interactions and interaction reasons. Such interactions may includebut are not limited to telephony calls, emails, chat sessions, textmessages, and other real-time and non-real time interactions.

The performance metric module 44 may contain instructions for computing,for each of various agents, one or more performance metrics associatedwith one or more different call topics. Each calculated performancemetric may be stored in the mass storage device 30 in association with,for example, the particular agent. The stored performance metrics may beused by the routing server 20 to select an appropriate agent to handle acall for which a call reason may be deduced. In this regard, instead ofrelying on general skill parameters that may be hard-coded into arouting strategy, more specific agent skills/sub-skills that are notpart of the hard-coded routing strategy may be considered along with theagent's track history in regards to such skills/sub-skills, inestimating the agent's performance to handle a call with a specific callreason, and routing the call to the agent with proficiency to handle thecall.

For example, a general skill assigned to an agent may be “billing” forhandling calls dealing with billing issues. If a call is identified asrelating to a sub-issue of “billing,” such as, for example, a doublecharge, changing a payment method, making a partial payment, or thelike, instead of routing the interaction to any one of various agentshaving the general “billing” skill set, embodiments of the presentinvention allow the call to be routed to an agent with the general skillset but who is also predicted to perform well in dealing with thespecific sub-issue. According to one embodiment, if an agent'sperformance consistently excels (e.g. is above a threshold) in varioussub-issues (e.g. for handling double charges, changing a payment method,making a partial payment, etc.), then the agent's general skill levelfor “billing,” may increase (e.g. from a level 3 to a level 4).Similarly, if an agent's performance consistently fails in varioussub-issues, then the agent's general skill level may decrease.

According to one embodiment, the various call topics deduced by the callinference module 42 may be accessible to the configuration server forbeing added to a set of skills and sub-skills that may be enabled ordisabled for a particular agent. For example, if a trend is detected ofpeople calling in regards to a hot topic (e.g. the last iPhone release),the topic may be added to a general set of skills that may be enabledfor an agent. Similarly, if no calls have been received for a period oftime in regards to a particular call topic, the call topic may beflagged far being removed from the general set of skill. According toone embodiment, the addition and/or removal of skills by theconfiguration server may be automatic. In other embodiments, theaddition and/or removal of skills does not occur until verified andallowed by an administrator.

According to one embodiment, detecting trends in regards to specificcall topics may cause other types of updating such as, for example,adding a dedicated telephone number that customers may call to inquireabout a specific call topic. In other embodiments, options provided bythe IMR server may also be updated in response to detecting a trend inspecific call topics. For example, an additional option directed to thespecific call topic may be provided to connect customers to agentsskilled to handle the specific call topic.

FIG. 2 is a flow diagram of a process executed by the performance metricmodule 44 in adding, adjusting, and/or removing (collectively referredto as “modifying”) a skill or sub-skill for a particular agent accordingto one embodiment of the invention. The term skill and sub-skill areused interchangeably herein. The process may be described in terms of asoftware routine executed by a processor based on instructions stored inmemory. The instructions may also be stored in other non-transientcomputer readable media such as, for example, a CD-ROM, flash drive, orthe like. A person of skill in the art should also recognize that theprocess may be executed via hardware, firmware (e.g. via an ASIC), or inany combination of software, firmware, and/or hardware. Furthermore, thesequence of steps of the process is not fixed, but can be altered intoany desired sequence as recognized by a person of skill in the art.

In act 100, the performance metric module 44 determines whether acondition for triggering evaluation of the agent's performance isdetected. For example, the evaluation may be triggered at particulartimes or intervals, such as, for example, on a weekly basis, every 30days, on specified days and/or times, after a certain number of callshave been handled by the agent, after a certain number of hours loggedby the agent, and/or the like. The invoking of the evaluation may beautomatic upon detecting the occurrence of a trigger condition. Theinvoking of the evaluation may also be manual, such as, for example, inresponse to a user command to start the evaluation.

If the condition for triggering evaluation of the agent's performance isdetected, the performance metric module 44 identifies a call topic inact 102. The call topic may be selected from a list of call topicscollected for a particular time period (e.g. in the last 30 days) basedon all (or a subset) of calls to the contact center, the agent, group ofagents, and/or the like. For example, the call inference module 42 maywork together with the speech analytics module 40 to collect call topicsof incoming interactions including telephony calls. The list of calltopics collected by the call inference module 42 may be stored in themass storage device 30 for retrieval by the performance metric module 44when needed.

In act 104, the performance metric module 44 determines whether theagent has handled a sufficient number of calls (e.g. at least 10)dealing with the identified call topic. The number may be set, forexample, by an administrator. If the answer is YES, the module 44, inact 106, calculates a performance metric for the call topic. Differenttypes of performances may be evaluated by the performance metric module.The type of performance that is evaluated may depend on theconfiguration of performance metric module 44. For example, theperformance metric module may be configured to evaluate the agent'sability to resolve an issue the first time the customer calls about theissue, referred to as first call resolution (FCR). Other types ofperformances may also be evaluated, such as, for example, handling time,customer satisfaction, sales conversions, debt collection, compliancewith regulations, customer retention (preventing churn), and the like.

Taking first call resolution as an example, a metric indicative of howthe agent is performing in this area may be calculated according to thefollowing formula:

$\begin{matrix}{{UNRESLV} = {\frac{\begin{pmatrix}{{{ALL\_ CALLS}{\_ ABOUT}{\_ X}}\bigcap} \\{{ALL\_ CALLS}{\_ UNRESOLVED}}\end{pmatrix}}{( {{ALL\_ CALLS}{\_ ABOUT}{\_ X}} )}*100\%}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

ALL_CALLS_ABOUT_X—all calls to the call center in the last 30 days (orother configurable time unit) handled by the particular agent that wereabout the topic X. For example, the topic may related to increasing thespending limit on a credit card.

ALL_CALLS_UNRESOLVED—all calls to the call center in the last 30 days(or other configurable time unit) that the particular agent handled thatwere classified as not resolved. A call may be classified as unresolvedif phrases such as, for example, “This doesn't help,” “I'll need to callagain,” or “Sorry that I couldn't help you with this,” are mentioned.Whether a call is resolved or not may be a binary determination—theanswer is either Yes or No. Other types of performances may not entail abinary determination.

According to equation 1, UNRESLV measures the particular agent's firstcall resolution performance in terms of percentage of unresolved callsfrom the overall calls on the particular topic X.

Taking handling time as another example, a metric indicative of how anagent is performing in this area may be calculated using an equationsimilar to equation 1. For example, the formula may be:

$\begin{matrix}{{HIGHHT} = {\frac{\begin{pmatrix}{{{ALL\_ CALLS}{\_ ABOUT}{\_ X}}\bigcap} \\{{ALL\_ CALLS}{\_ HIGHHNDLETIME}}\end{pmatrix}}{( {{ALL\_ CALLS}{\_ ABOUT}{\_ X}} )}*100\%}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

According to equation 2, HIGHHT measures the particular agent's handlingtime performance in terms of percentage of calls with high handling timefrom the overall calls on the particular topic X. Here, the variableALL_CALLS_HIGHHENDLTIME are all calls to the call center in the last 30days (or other configurable time unit) that the particular agent handledwhere the handling time was above a threshold time (e.g. above 5minutes).

According to another embodiment, measurement of an agent's handling timeperformance may not entail a binary determination as in equation 2.Instead, the performance value may be the actual average handling timeof an agent when handling calls relating to a particular topic. Anaverage handling time performance value may be, for example, 5.2minutes.

According to one embodiment, the metric that is calculated in act 106may be a short term metric that takes into account the agent'sperformance over a short term period. For example, the agent'sperformance may be calculated afresh every 30 days. According to oneembodiment, a historical (long term) metric may also be calculated inaddition or in lieu of the short term metric. For example, thehistorical metric may be an average of all past short term metricscalculated for the agent.

In act 107, the agent's skill information is modified based on thecalculated metric. In this regard, the performance metric module 44retrieves the skills and associated values stored for the agent in themass storage device 30. If the call topic is already enabled as a skillfor the particular agent, the performance metric module 44 determineswhether the currently calculated metric differs from the stored metric.If so, the stored metric is replaced with the currently calculatedmetric. If the call topic is not already enabled as a skill for theparticular agent, the skill is added/enabled as a new skill for theagent, and the calculated metric is stored in association with the addedskill.

According to an embodiment of the invention, the performance metricmodule 44 may be configured to modify a performance level of a generalskill based on knowledge on actual performance on sub-skills related tothe general skill. For example, if an agent shows a trend of performingabove a threshold level on all (or a portion of) the sub-skills, theperformance level of the general skill may be increased. Similarly, ifthe agent shows a trend of performing below a threshold level on all (ora portion of) the sub-skills, the performance level of the general skillmay be decreased.

FIG. 3 is a conceptual layout diagram of exemplary skills and sub-skillsthat may be enabled or disabled for a specific agent according to oneembodiment of the invention. In the example of FIG. 3, Agent Smithlogged on to directory number (DN) 888-888-8888. Agent Smith has a“credit card online technical support” skill 150 that is enabled while a“new credit card” skill 158 is not enabled. The “credit card onlinetechnical support” skill 150 is assigned a level 3 (e.g. out of 5),indicative of the agent's proficiency in handling this general skill.According to one embodiment, the initial skill level assignment forgeneral skills may be based on certifications received by the agent,based on tests taken by the agent, or the like.

In the example of FIG. 3, various sub-skills are enabled for AgentSmith, including “password change” 152 and “eBills” 156. The enabledsub-skills 152, 156 indicate that the agent has had experience inhandling calls relating to the specific call topics. Other sub-skills,such as, for example, “account alerts” 154, are not enabled, indicatingthat the agent has not shown enough of a track record in handling callsrelating to this topic.

In the example of FIG. 3, three performance measurements are shown foreach enabled sub-skill/call topic 152, 154. Such performancemeasurements relate to unresolved calls 160 a, 160 b, average handlingtime 162 a, 162 b, and customer satisfaction 164 a, 164 b. Agent Smithin the example of FIG. 3 has a performance metric of 40% for unresolvedcalls when dealing with the call topic “password change,” while he has aperformance metric of only 10% for unresolved calls when dealing withthe call topic “eBills.” Thus, agent Smith is more proficient, at leastin terms of first time call resolution, when dealing with calls abouteBills than dealing with calls about password change.

In regards to average handle time, the example of FIG. 3 shows that ittakes Agent Smith an average of 5.2 minutes in handling calls aboutpassword change while it takes him an average of 5 minutes in handlingcalls about eBills. For customer satisfaction, Agent Smith gets anaverage rating of 2 out of 5 from customers calling about passwordchange, while he gets an average rating of 4.5 out of 5 from customerscalling about eBills.

FIG. 4 is a conceptual layout diagram of a call distribution table 200that may be populated by the call inference module 42 or speechanalytics module 40 according to one embodiment of the invention. Thecall distribution table 200 includes a list of call topics/categories202 inferred for the calls received by the call center, a specificagent, a department, or group of agents, during a particular timeperiod. The categories relate to specific call topics, such as, forexample, billing issue, equipment issue, and the like. The calldistribution table 200 further includes a percentage of calls 204, totalnumber of calls 206, and average duration of the calls 208 detected forthe particular time period. Other information may also be maintained inthe call distribution table, such as, for example, a time period duringwhich the listed call topics where detected, identifiers of agentshandling each category, and the like. In the embodiment that the calldistribution table 200 is specific to a particular agent, the table maybe stored in association with an agent ID and/or DN.

According to one embodiment, an incoming call may be classified under aparticular call topic/category 202 based on recognition of a call asbeing a repeat call. For example, if a call is a repeat call and a priorcustomer issue was not resolved during a prior call, an inference may bemade, at least initially, that the call reason for the current call isthe same as the unresolved issue from the prior call. For instance if ina prior call a caller asked the agent “How do I program my DVR from mymobile phone,” and the agent's answer was “I can't help you with this,you might want to visit our website,” the speech analytics module may beconfigured to analyze the content of the call, recognize the specificutterances, and classify the call reason as being “DVR Programming.” Thespeech analytics module may further classify the call as “unresolved.”The classification information is stored in the customer database. Whenthe caller calls again, the customer database may be inquired toretrieve a most recent unresolved call reason, and inquire the callerwhether the caller is calling to resolve the issue. For example, the IMRserver 34 may be prompted to ask, “I see that you had called before toinquire about programming your DVR. Would you like to be connected to anagent who can help you with this?” If the customer responds “Yes,” thenthe prior call reason is set as the current call reason.

Of course, as a person of skill in the art should appreciate, the callreason for a repeat call may also be deduced without invoking the IMRserver For example, if the repeat call is received within a certainperiod of time (e.g. within 15 minutes) of an unresolved call, anassumption may be automatically made that the caller is calling aboutthe unresolved issue (e.g. “DVR Programming”) and not request expressconfirmation from the caller. In this case, the prior unresolved callreason is automatically set as the current call reason.

A call reason may also be inferred based on the customer's interactionwith the IMR server 34 (FIG. 1). For example, the IMR server 34 may beconfigured to ask the customer an open-ended question to elicitinformation on the reason for the call. For example, the IMR server 34may be configured to ask “How can I help you?” The customer may answerby speaking a reason for the call, such as, for example, “Am I eligibleto upgrade my phone?” According to one embodiment, the phrase uttered bythe customer is analyzed by the speech analytics module 40 and the callis then categorized into a particular category, such as, for example, an“upgrade eligibility” category. In other embodiments, the question askedby the IMR server 34 is not open-ended, but limits the caller's responseto, for example, a particular subject. Of course, the IMR server 34 mayalso be configured to function as a traditional IVR where the customeris provided a list of reasons for the call, and the customer presses anumber that matches his or her reason for the call. The call reason maybe deduced from the particular number pressed by the customer.

In other embodiments, a call reason may also be deduced based on othermodalities, such as, for example, the customer's interactions usingother communication channels, such as, for example, the Web, retailshops, email, chat channels, mobile applications, social media channels,and the like. Multi-channel analytical solutions such as, for example,Genesys Text Analytics offered by Genesys TelecommunicationsLaboratories, Inc., may be used to analyze a customer's previousengagement to identify a previous problem and/or needs before connectinga current call to a contact center agent. For example, a customer may bebrowsing a company web page relating to “Flying with Pets Policy” whenthe customer selects a “speak with an agent” option. Information on thepage being browsed may be transmitted along with the request to speakwith the agent, and from the transmitted information it may be inferredthat the customer's reason for wanting to speak with the agent isrelated to “Flying with Pets.”

FIG. 5 is a conceptual layout diagram of a process engaged by the speechanalytics module 40 for assigning topics based on specific spokenphrases detected during a conversation between a customer and an agentaccording to one embodiment of the invention. The phrases may berecorded phrases from a prior call, or phrases detected in real-timeduring a current call (e.g. while the caller is interacting with the IMRserver 34). According to one embodiment, each topic is defined as unionof phrases. If a caller says “Where's my order?” or “I haven't receivedthis shipment,” then the call is classified as belonging to the “where'smy stuff?” topic. If during the same call the caller says “This is notanswering my question,” or “I will have to call back,” or the agent says“I can't help you with this,” the call is also classified as belongingto an “unresolved” topic. Calls may be classified based on detectedphrases according to the methodology described in U.S. Pat. No.7,487,094, the content of which is incorporated herein by reference. Byclassifying the call reason and whether the call has been resolved ornot, and storing the information in, for example, a customer database,the speech analytics module 40 generates relevant information that isused for more optimal routing of calls based on an agent's performancein handling calls with the identified issues.

FIG. 6 is a flow diagram of a process for performance-based routing ofinteractions to agents according to one embodiment of the invention.Although real-time inbound calls is used as an example, a person ofskill in the art should recognize that the process may also extend tonon-real-time tasks, such as, for example responding to emails. Theprocess may be described in terms of a software routine executed by aprocessor based on instructions stored in memory. The instructions mayalso be stored in other non-transient computer readable media such as,for example, a CD-ROM, flash drive, or the like. A person of skill inthe art should also recognize that the process may be executed viahardware, firmware (e.g. via an ASIC), or in any combination ofsoftware, firmware, and/or hardware. Furthermore, the sequence of stepsof the process is not fixed, but can be altered into any desiredsequence as recognized by a person of skill in the art.

The process starts, and in act 200, an inbound interaction to aparticular route point is received, and a message is transmitted toinform, for example, the call server 18 (for telephony calls) or themultimedia server 24 (for other multimedia interactions) of theinteraction. The message may include information on the interaction suchas, for example, for telephony interactions, the telephone number thatwas dialed and/or telephone number of the calling customer. If theinteraction is a non-telephony interaction, such as, for example, anemail, the information that is included in the message may be the senderand recipient's email addresses.

According to one embodiment, the routing server 20 is invoked to routethe interaction to an appropriate contact center resource. In thisregard, the routing server 20 may be configured to execute a routingstrategy configured for the particular route point for routing theinteraction. The routing strategy may, for example, invoke the callinference module 42 to deduce the reason or topic for the call in orderto identify agents with skills on a more granular level than the skillsthat may be hardcoded into the particular routing strategy.

In act 202, the call inference module 42 may be configured to useinformation gathered about the interaction (e.g. customer's telephonenumber) to perform a lookup in the customer database for information onthe customer and his/her past interactions. Based on the lookup, thecall inference module 42 may deduce, in act 204, the call reason/topicfor the current interaction. For example, lookup of the customerdatabase may indicate that the current call is associated with a priorcall (e.g. it is a repeat call), and that the prior call dealing with aparticular issue was unresolved. The call inference module 42 may alsobe configured to infer the call reason based on information provided byother modalities, such as, for example, the IMR server 34. The inferredcall reason may be returned to the routing server 20 for use in routingthe current interaction.

In act 206, the routing strategy invoked by the routing server 20 mayinquire whether agents exist that have experience in handling theparticular call reason. For example, instead of looking for agents witha general skill set (e.g. agents with level 3 or higher for “billing”),the routing strategy may be configured to first look for agents with theskill set “billing” but who also have specific sub-skills enabled under“billing,” where the sub-skills match the inferred call reason, such as,for example, “double charges,” indicating that the agent has dealt withthis particular call topic in the past.

If one or more available agents exist with the specific sub-skillenabled, the routing server 20 may determine, in act 208, whether thereis at least one agent who has demonstrated at least a minimum level ofproficiency in dealing with the particular call topic. According to oneembodiment, the routing server 20 determines, in act 208, whether thereis at least one agent whose proficiency level for the particular calltopic satisfies a threshold proficiency level. The threshold proficiencylevel may be user defined, and may vary depending on the type ofproficiency that is measured. Also, the type of proficiency that isconsidered may vary depending on the type of calls that are beinghandled. For example, in some situations, the proficiency that isconsidered may be an agent's first call resolution. In this example, theagent's proficiency level may measure a percentage of unresolved callsby the agent dealing with particular call topics. In other situations,the proficiency that is considered may be an average handling time,and/or customer satisfaction ratings. A combination of the differenttypes of proficiencies may also be considered. For example, the routingserver 20 may be configured to look for an agent with at least a minimumlevel of proficiency in all (or a portion) of the different types ofproficiencies to give preference to that agent in handling a particulartype of call.

If there are one or more agents who have demonstrated at least a minimumlevel of proficiency for the identified sub-skill, the routing server 20is configured, in act 210, to select from the identified agents andinstruct the call to be routed to the selected agent. According to oneembodiment, if there are multiple agents with at least a minimum levelproficiency, the agent that is actually selected may depend on theparticular selection algorithm that is employed. For example, theselection algorithm may rank the identified agents based on theirproficiency levels and give priority to agents with the higherproficiency levels before routing calls to agents with the lowerproficiency levels. In other examples, the selection algorithm maysimply select an agent randomly or on a round robin basis. The selectionalgorithm may also select an agent based on the type of customer that isto be serviced (e.g. highest performing agent for a Gold customer, and anext highest performing agent for a Silver customer). Thus, takingbilling as an example, given a set of agents having the skill set todeal with billing issues generally, if a call is for a specific type ofbilling issue (e.g. double charge), the call may be routed to an agenthaving more proficiency in dealing with double charge issues thatanother agent, also proficient in billing generally, but less proficientin dealing with double charge issues.

Referring again to acts 206 and 208, if either there are no agents whohave experience handling interactions dealing with the identified callreason, or even if such agents exist, if none have demonstrated at leasta minimum level of proficiency in handling these types of interactions,the routing server 20 is configured to, in act 212, route the call to anavailable agent based on general skill sets identified in the routingstrategy. Of course, if no such agent exists, the interaction is routedto any available agent.

FIG. 7 is a conceptual layout diagram of exemplary proficiency levels ofagents where the proficiency is first call resolution for a particularcall topic (not shown). In order to determine an agent's first callresolution proficiency, a measurement of a percentage of calls thatterminate without resolving the call issue is monitored and tracked foreach agent. In the exemplary diagram, agent CW 300 is the mostproficient with 18.8% of calls he has handled terminating withoutresolution. Agent IQ 302, on the other hand, is the least proficientwith 45.9% of calls terminating without resolution. In this example, theaverage proficiency level is 31.1%, and this may be used as thethreshold proficiency before an agent is considered for handling thespecific call topic. According to this example, agents DV 304, HR 306,IQ 302, and JP 308 have unresolved calls above the threshold level of31.1%. Thus, according to one embodiment, if other agents with a higherproficiency are available (e.g. CW 300), a call with the specific calltopic is routed to the agents with the higher proficiency.

Each of the various servers, controllers, switches, gateways, engines,and/or modules (collectively referred to as servers) in theafore-described figures may be a process or thread, running on one ormore processors, in one or more computing devices 1500 (e.g., FIG. 8A,FIG. 8B), executing computer program instructions and interacting withother system components for performing the various functionalitiesdescribed herein. The computer program instructions are stored in amemory which may be implemented in a computing device using a standardmemory device, such as, for example, a random access memory (RAM). Thecomputer program instructions may also be stored in other non-transitorycomputer readable media such as, for example, a CD-ROM, flash drive, orthe like. Also, a person of skill in the art should recognize that acomputing device may be implemented via firmware (e.g. anapplication-specific integrated circuit), hardware, or a combination ofsoftware, firmware, and hardware. A person of skill in the art shouldalso recognize that the functionality of various computing devices maybe combined or integrated into a single computing device, or thefunctionality of a particular computing device may be distributed acrossone or more other computing devices without departing from the scope ofthe exemplary embodiments of the present invention. A server may be asoftware module, which may also simply be referred to as a module. Theset of modules in the contact center may include servers, and othermodules.

The various servers may be located on a computing device on-site at thesame physical location as the agents of the contact center or may belocated off-site (or in the cloud) in a geographically differentlocation, e.g., in a remote data center, connected to the contact centervia a network such as the Internet. In addition, some of the servers maybe located in a computing device on-site at the contact center whileothers may be located in a computing device off-site, or serversproviding redundant functionality may be provided both via on-site andoff-site computing devices to provide greater fault tolerance. In someembodiments of the present invention, functionality provided by serverslocated on computing devices off-site may be accessed and provided overa virtual private network (VPN) as if such servers were on-site, or thefunctionality may be provided using a software as a service (SaaS) toprovide functionality over the internet using various protocols, such asby exchanging data using encoded in extensible markup language (XML) orJavaScript Object notation (JSON).

FIG. 8A and FIG. 8B depict block diagrams of a computing device 1500 asmay be employed in exemplary embodiments of the present invention. Eachcomputing device 1500 includes a central processing unit 1521 and a mainmemory unit 1522. As shown in FIG. 8A, the computing device 1500 mayalso include a storage device 1528, a removable media interface 1516, anetwork interface 1518, an input/output (I/O) controller 1523, one ormore display devices 1530 c, a keyboard 1530 a and a pointing device1530 b, such as a mouse. The storage device 1528 may include, withoutlimitation, storage for an operating system and software. As shown inFIG. 8B, each computing device 1500 may also include additional optionalelements, such as a memory port 1503, a bridge 1570, one or moreadditional input/output devices 1530 d, 1530 e and a cache memory 1540in communication with the central processing unit 1521. The input/outputdevices 1530 a, 1530 b, 1530 d, and 1530 e may collectively be referredto herein using reference numeral 1530.

The central processing unit 1521 is any logic circuitry that responds toand processes instructions fetched from the main memory unit 1522. Itmay be implemented, for example, in an integrated circuit, in the formof a microprocessor, microcontroller, or graphics processing unit (GPU),or in a field-programmable gate array (FPGA) or application-specificintegrated circuit (ASIC). The main memory unit 1522 may be one or morememory chips capable of storing data and allowing any storage locationto be directly accessed by the central processing unit 1521. As shown inFIG. 8A, the central processing unit 1521 communicates with the mainmemory 1522 via a system bus 1550. As shown in FIG. 8B, the centralprocessing unit 1521 may also communicate directly with the main memory1522 via a memory port 1503.

FIG. 8B depicts an embodiment in which the central processing unit 1521communicates directly with cache memory 1540 via a secondary bus,sometimes referred to as a backside bus. In other embodiments, thecentral processing unit 1521 communicates with the cache memory 1540using the system bus 1550. The cache memory 1540 typically has a fasterresponse time than main memory 1522. As shown in FIG. 8A, the centralprocessing unit 1521 communicates with various I/O devices 1530 via thelocal system bus 1550. Various buses may be used as the local system bus1550, including a Video Electronics Standards Association (VESA) Localbus (VLB), an Industry Standard Architecture (ISA) bus, an ExtendedIndustry Standard Architecture (EISA) bus, a MicroChannel Architecture(MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI Extended(PCI-X) bus, a PCI-Express bus, or a NuBus. For embodiments in which anI/O device is a display device 1530 c, the central processing unit 1521may communicate with the display device 1530 c through an AdvancedGraphics Port (AGP). FIG. 8B depicts an embodiment of a computer 1500 inwhich the central processing unit 1521 communicates directly with I/Odevice 1530 e. FIG. 8B also depicts an embodiment in which local bussesand direct communication are mixed: the central processing unit 1521communicates with I/O device 1530 d using a local system bus 1550 whilecommunicating with I/O device 1530 e directly.

A wide variety of I/O devices 1530 may be present in the computingdevice 1500. Input devices include one or more keyboards 1530 a, mice,trackpads, trackballs, microphones, and drawing tablets. Output devicesinclude video display devices 1530 c, speakers, and printers. An I/Ocontroller 1523, as shown in FIG. 8A, may control the I/O devices. TheI/O controller may control one or more I/O devices such as a keyboard1530 a and a pointing device 1530 b, e.g., a mouse or optical pen.

Referring again to FIG. 8A, the computing device 1500 may support one ormore removable media interfaces 1516, such as a floppy disk drive, aCD-ROM drive, a DVD-ROM drive, tape drives of various formats, a USBport, a Secure Digital or COMPACT FLASH™ memory card port, or any otherdevice suitable for reading data from read-only media, or for readingdata from, or writing data to, read-write media. An I/O device 1530 maybe a bridge between the system bus 1550 and a removable media interface1516.

The removable media interface 1516 may for example be used forinstalling software and programs. The computing device 1500 may furthercomprise a storage device 1528, such as one or more hard disk drives orhard disk drive arrays, for storing an operating system and otherrelated software, and for storing application software programs.Optionally, a removable media interface 1516 may also be used as thestorage device. For example, the operating system and the software maybe run from a bootable medium, for example, a bootable CD.

In some embodiments, the computing device 1500 may comprise or beconnected to multiple display devices 1530 c, which each may be of thesame or different type and/or form. As such, any of the I/O devices 1530and/or the I/O controller 1523 may comprise any type and/or form ofsuitable hardware, software, or combination of hardware and software tosupport, enable or provide for the connection to, and use of, multipledisplay devices 1530 c by the computing device 1500. For example, thecomputing device 1500 may include any type and/or form of video adapter,video card, driver, and/or library to interface, communicate, connect orotherwise use the display devices 1530 c. In one embodiment, a videoadapter may comprise multiple connectors to interface to multipledisplay devices 1530 c. In other embodiments, the computing device 1500may include multiple video adapters, with each video adapter connectedto one or more of the display devices 1530 c. In some embodiments, anyportion of the operating system of the computing device 1500 may beconfigured for using multiple display devices 1530 c. In otherembodiments, one or more of the display devices 1530 c may be providedby one or more other computing devices, connected, for example, to thecomputing device 1500 via a network. These embodiments may include anytype of software designed and constructed to use the display device ofanother computing device as a second display device 1530 c for thecomputing device 1500. One of ordinary skill in the art will recognizeand appreciate the various ways and embodiments that a computing device1500 may be configured to have multiple display devices 1530 c.

A computing device 1500 of the sort depicted in FIG. 8A and FIG. 8B mayoperate under the control of an operating system, which controlsscheduling of tasks and access to system resources. The computing device1500 may be running any operating system, any embedded operating system,any real-time operating system, any open source operating system, anyproprietary operating system, any operating systems for mobile computingdevices, or any other operating system capable of running on thecomputing device and performing the operations described herein.

The computing device 1500 may be any workstation, desktop computer,laptop or notebook computer, server machine, handheld computer, mobiletelephone or other portable telecommunication device, media playingdevice, gaming system, mobile computing device, or any other type and/orform of computing, telecommunications or media device that is capable ofcommunication and that has sufficient processor power and memorycapacity to perform the operations described herein. In someembodiments, the computing device 1500 may have different processors,operating systems, and input devices consistent with the device.

In other embodiments the computing device 1500 is a mobile device, suchas a Java-enabled cellular telephone or personal digital assistant(PDA), a smart phone, a digital audio player, or a portable mediaplayer. In some embodiments, the computing device 1500 comprises acombination of devices, such as a mobile phone combined with a digitalaudio player or portable media player.

As shown in FIG. 8C, the central processing unit 1521 may comprisemultiple processors P1, P2, P3, P4, and may provide functionality forsimultaneous execution of instructions or for simultaneous execution ofone instruction on more than one piece of data. In some embodiments, thecomputing device 1500 may comprise a parallel processor with one or morecores. In one of these embodiments, the computing device 1500 is ashared memory parallel device, with multiple processors and/or multipleprocessor cores, accessing all available memory as a single globaladdress space. In another of these embodiments, the computing device1500 is a distributed memory parallel device with multiple processorseach accessing local memory only. In still another of these embodiments,the computing device 1500 has both some memory which is shared and somememory which may only be accessed by particular processors or subsets ofprocessors. In still even another of these embodiments, the centralprocessing unit 1521 comprises a multicore microprocessor, whichcombines two or more independent processors into a single package, e.g.,into a single integrated circuit (IC). In one exemplary embodiment,depicted in FIG. 8D, the computing device 1500 includes at least onecentral processing unit 1521 and at least one graphics processing unit1521′.

In some embodiments, a central processing unit 1521 provides singleinstruction, multiple data (SIMD) functionality, e.g., execution of asingle instruction simultaneously on multiple pieces of data. In otherembodiments, several processors in the central processing unit 1521 mayprovide functionality for execution of multiple instructionssimultaneously on multiple pieces of data (MIMD). In still otherembodiments, the central processing unit 1521 may use any combination ofSIMD and MIMD cores in a single device.

A computing device may be one of a plurality of machines connected by anetwork, or it may comprise a plurality of machines so connected. FIG.8E shows an exemplary network environment. The network environmentcomprises one or more local machines 1502 a, 1502 b (also generallyreferred to as local machine(s) 1502, client(s) 1502, client node(s)1502, client machine(s) 1502, client computer(s) 1502, client device(s)1502, endpoint(s) 1502, or endpoint node(s) 1502) in communication withone or more remote machines 1506 a, 1506 b, 1506 c (also generallyreferred to as server machine(s) 1506 or remote machine(s) 1506) via oneor more networks 1504. In some embodiments, a local machine 1502 has thecapacity to function as both a client node seeking access to resourcesprovided by a server machine and as a server machine providing access tohosted resources for other clients 1502 a, 1502 b. Although only twoclients 1502 and three server machines 1506 are illustrated in FIG. 8E,there may, in general, be an arbitrary number of each. The network 1504may be a local-area network (LAN), e.g., a private network such as acompany Intranet, a metropolitan area network (MAN), or a wide areanetwork (WAN), such as the Internet, or another public network, or acombination thereof.

The computing device 1500 may include a network interface 1518 tointerface to the network 1504 through a variety of connectionsincluding, but not limited to, standard telephone lines, local-areanetwork (LAN), or wide area network (WAN) links, broadband connections,wireless connections, or a combination of any or all of the above.Connections may be established using a variety of communicationprotocols. In one embodiment, the computing device 1500 communicateswith other computing devices 1500 via any type and/or form of gateway ortunneling protocol such as Secure Socket Layer (SSL) or Transport LayerSecurity (TLS). The network interface 1518 may comprise a built-innetwork adapter, such as a network interface card, suitable forinterfacing the computing device 1500 to any type of network capable ofcommunication and performing the operations described herein. An I/Odevice 1530 may be a bridge between the system bus 1550 and an externalcommunication bus.

It is the Applicant's intention to cover by claims all such uses of theinvention and those changes and modifications which could be made to theembodiments of the invention herein chosen for the purpose of disclosurewithout departing from the spirit and scope of the invention. Theparticular manner in which template details are presented to the usermay also differ. Thus, the present embodiments of the invention shouldbe considered in all respects as illustrative and not restrictive, thescope of the invention to be indicated by claims and their equivalentsrather than the foregoing description.

1. A method for performance-based routing of interactions in a contactcenter comprising: receiving, by a processor, information on aninteraction to be routed; identifying, by the processor, a topicassociated with the interaction; identifying, by the processor, one ormore agents having experience handling the topic; determining, by theprocessor, a proficiency level of the identified agents in handling thetopic; selecting, by the processor, one of the identified agents havingat least a minimum level of proficiency in handling the topic; andtransmitting a message, by the processor, for routing the interaction tothe selected agent.
 2. The method of claim 1, wherein the proficiencylevel of a particular agent is calculated based on past interactions bythe particular agent in handling interactions with a same topic.
 3. Themethod of claim 1, wherein the proficiency level of a particular agentis indicative of the ability of the particular agent to resolve thetopic a first time the topic is raised in an interaction.
 4. The methodof claim 3 further comprising: recognizing, by the processor, a phraseuttered by a customer or agent during a past interaction; categorizing,by the processor, the past interaction as being unresolved based on therecognized phrase; and calculating, by the processor, the proficiencylevel based on a number of past interactions categorized as beingunresolved.
 5. The method of claim 1, wherein the proficiency level of aparticular agent is indicative of a time it takes the agent in handlinginteractions associated with the topic.
 6. The method of claim 1,wherein the topic is identified as a specific agent skill, whereinidentifying by the processor the one or more agents includes identifyingby the processor one or more agents with the specific agent skillenabled.
 7. The method of claim 1, wherein the selecting of one of theidentified agents having at least a minimum level of proficiencyincludes: identifying, by the processor, a threshold performance value;comparing, by the processor, the proficiency level of the identifiedagents against the threshold performance value; and selecting, by theprocessor, the one of the identified agents based on the comparison. 8.The method of claim 1, wherein the identifying of the topic includes:recognizing a phrase uttered by a customer or agent; and identifying thetopic based on the recognized phrase.
 9. The method of claim 8, whereinthe phrase is uttered during interaction by the customer with aninteractive media response server.
 10. A system for performance-basedrouting of interactions in a contact center comprising: processor; andmemory, the memory storing instructions that, when executed by theprocessor, cause the processor to: identify information on aninteraction to be routed; identify a topic associated with theinteraction; identify one or more agents having experience handling thetopic; determine a proficiency level of the identified agents inhandling the topic; select one of the identified agents having at leasta minimum level of proficiency in handling the topic; and transmit amessage for routing the interaction to the selected agent.
 11. Thesystem of claim 10, wherein the proficiency level of a particular agentis calculated based on past interactions by the particular agent inhandling interactions with a same topic.
 12. The system of claim 10,wherein the proficiency level of a particular agent is indicative of theability of the particular agent to resolve the topic a first time thetopic is raised in an interaction.
 13. The system of claim 12, whereinthe instructions further cause the processor to: recognize a phraseuttered by a customer or agent during a past interaction; categorize thepast interaction as being unresolved based on the recognized phrase; andcalculate the proficiency level based on a number of past interactionscategorized as being unresolved.
 14. The system of claim 10, wherein theproficiency level of a particular agent is indicative of a time it takesthe agent in handling interactions associated with the topic.
 15. Thesystem of claim 10, wherein the topic is identified as a specific agentskill, wherein identifying by the processor of the one or more agentsincludes identifying by the processor one or more agents with thespecific agent skill enabled.
 16. The system of claim 10, wherein theinstructions that cause the processor to select one of the identifiedagents having at least a minimum level of proficiency includesinstructions that cause the processor to: identify a thresholdperformance value; compare the proficiency level of the identifiedagents against the threshold performance value; and select the one ofthe identified agents based on the comparison.
 17. The system of claim10, wherein the instructions the cause the processor to identify thetopic further includes instructions that cause the processor to:recognize a phrase uttered by a customer or agent; and identify thetopic based on the recognized phrase.
 18. The system of claim 17,wherein the phrase is uttered during interaction by the customer with aninteractive media response server.